PB1826: A NEW WIDELY APPLICABLE MODEL COMBINED MOLECULAR AND CLINICAL PROGNOSTIC INDEX FOR OVERALL SURVIVAL AND RISK STRATIFICATION IN ADULTS WITH ACUTE MYELOID LEUKEMIA
Authors: J. Ma, Z. Ge
Journal: HemaSphere (2022)

Background: The 2017 European LeukemiaNet (ELN) risk stratification combines cytogenetic abnormalities and genetic mutations to provide risk-stratifying optimal therapies for patients with acute myeloid leukemia (AML). However, due to the lack of full cytogenetic and genetic mutation files at diagnosis, the ELN-2017 applicability in low- and middle-income countries is limited. Additionally, many clinical prognostic factors are not included in the risk stratification. Aims: This study is to develop a widely applicable prognostic model by combining molecular and clinical characteristics based on the real-world data. Methods: The Adapted ELN guideline ([AELN]) was used by adjusting some molecular genetic abnormalities and allowing the missing cytogenetic or molecular data to improve the performance and expand adaptability of the guidelines. The training cohort of 183 de novo adult AML (non APL) patients was conducted at Zhongda Hospital of Southeast University. The validation cohort of 103 AML patients was obtained from Xuzhou Central Hospital. Univariate Cox, multivariate Cox, Best Subset Regression (BSR) and LASSO regression analyses were used to determine the significant factors that influenced overall survival (OS). We chose the final model that yielded the largest AUC or the smallest AIC. The receiver operating characteristic curve (ROC) was used to evaluate the efficiency of the prognostic model. Kaplan-Meier (K-M) survival curve with Log Rank analysis were performed in the study. Results: Median follow-up was 39.6 (95%CI: 32.9-48.9, reverse K-M) and 33.6 (22.7-45.8) months for training and validation cohort, respectively. Median OS was 15.7 (12.9-15.9, K-M) and 16.9 (11.9-46.6) months, respectively. AELN risk classification was statistically significant for OS in both training (P<0.0001) and validation cohorts (P=0.016). AELN prognostic classification was more superior in sensitivity and accuracy than ELN-2010 in distinguishing different risk group categories with the AUC value of 0.724 vs. 0.631 respectively in the training cohort. The AUC of BSR (0.75) or LASSO (0.75) regression analysis was higher than univariate Cox analysis (0.737). In this study, we optimized the modeling methods, and LASSO combined with multivariate Cox analysis were selected eventually. On the basis of age, WBC count, and AELN risk classification, patients were classified into low-risk (LR, ≤1.57) and high-risk (HR, >1.57) groups according to the AMLOS score model [AMLOS score =age (years) × 0.03 + WBC count (× 109/L) ×10-9 × 0.004 + 1.15 (if AELN poor, otherwise 0)] with median OS of 46.3 months and 31.4 months in LR and HR groups, respectively (P<0.0001; Fig. 1A). C-statistic in training cohort was 0.706 (95% CI: 0.66-0.76). Meanwhile, in validation cohort, the OS was significantly different between LR and HR (P=0.015, Fig. 1B) with the median OS of 87.5 and 12.3 months, respectively. Compared with AELN risk classification, AMLOS score model was more superior in both sensitivity and applicability with the AUC value in AMLOS vs. AELN alone of 0.716 vs. 0.693, 0.702 vs. 0.531 in training cohort and validation cohort, respectively (Fig. 1C, D). Image:Summary/Conclusion: AELN is a more applicable genetic risk classification tool for low-income groups. AMLOS, the integral risk stratification system developed by integrating AELN and the clinical prognostic factors, is a widely applicable prognostic model for treatment guidance in adult patients with AML.